Cloud–Fog–Based Smart Grid Model for Efficient Resource Management

Zahoor, Saman; Javaid, Sakeena; Javaid, Nadeem; Ashraf, Mahmood; Ishmanov, Farruh; Afzal, Muhammad Khalil

doi:10.3390/su10062079

Cited by 79 publications

(44 citation statements)

References 29 publications

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“…The simulations are performed for 24 h (a day) using the CloudAnalyst tool. The load and size of requests from the consumers, number, and size of VMs, hardware and virtual specifications of fog computing are initialized according to [39,40].…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

et al. 2018

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The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud-fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

et al. 2018

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“…The ABC approach was successfully tested on a wider set of benchmark functions [35,36] and it also has many implementations for practical NP-hard tasks [37][38][39][40]. Moreover, the ABC algorithm has been applied to problems from the cloud computing field [41]. As one representative example, in [42], the authors developed a state-of-the-art hybrid ABC approach that managed to substantially reduce energy consumption while performing VM migration.…”

Section: Review Of Swarm Intelligence Metaheuristics and Its Applicatmentioning

confidence: 99%

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

Strumberger

Tuba

Bačanin

et al. 2019

JSAN

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Cloud computing technology enables efficient utilization of available physical resources through the virtualization where different clients share the same underlying physical hardware infrastructure. By utilizing the cloud computing concept, distributed, scalable and elastic computing resources are provided to the end-users over high speed computer networks (the Internet). Cloudlet scheduling that has a significant impact on the overall cloud system performance represents one of the most important challenges in this domain. In this paper, we introduce implementations of the original and hybridized monarch butterfly optimization algorithm that belongs to the category of swarm intelligence metaheuristics, adapted for tackling the cloudlet scheduling problem. The hybridized monarch butterfly optimization approach, as well as adaptations of any monarch butterfly optimization version for the cloudlet scheduling problem, could not be found in the literature survey. Both algorithms were implemented within the environment of the CloudSim platform. The proposed hybridized version of the monarch butterfly optimization algorithm was first tested on standard benchmark functions and, after that, the simulations for the cloudlet scheduling problem were performed using artificial and real data sets. Based on the obtained simulation results and the comparative analysis with six other state-of-the-art metaheuristics and heuristics, under the same experimental conditions and tested on the same problem instances, a hybridized version of the monarch butterfly optimization algorithm proved its potential for tackling the cloudlet scheduling problem. It has been established that the proposed hybridized implementation is superior to the original one, and also that the task scheduling problem in cloud environments can be more efficiently solved by using such an algorithm with positive implications to the cloud management.

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“…However, a user has to give upfront payment for reserved instance. Javaid, S. et al proposed a hybrid-genetic-wind-driven algorithm in [15]. The authors performed this work to schedule the electricity load between on-peak and off-peak hours.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

et al. 2019

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Cloud computing offers various services. Numerous cloud data centers are used to provide these services to the users in the whole world. A cloud data center is a house of physical machines (PMs). Millions of virtual machines (VMs) are used to minimize the utilization rate of PMs. There is a chance of unbalanced network due to the rapid growth of Internet services. An intelligent mechanism is required to efficiently balance the network. Multiple techniques are used to solve the aforementioned issues optimally. VM placement is a great challenge for cloud service providers to fulfill the user requirements. In this paper, an enhanced levy based multi-objective gray wolf optimization (LMOGWO) algorithm is proposed to solve the VM placement problem efficiently. An archive is used to store and retrieve true Pareto front. A grid mechanism is used to improve the non-dominated VMs in the archive. A mechanism is also used for the maintenance of an archive. The proposed algorithm mimics the leadership and hunting behavior of gray wolves (GWs) in multi-objective search space. The proposed algorithm was tested on nine well-known bi-objective and tri-objective benchmark functions to verify the compatibility of the work done. LMOGWO was then compared with simple multi-objective gray wolf optimization (MOGWO) and multi-objective particle swarm optimization (MOPSO). Two scenarios were considered for simulations to check the adaptivity of the proposed algorithm. The proposed LMOGWO outperformed MOGWO and MOPSO for University of Florida 1 (UF1), UF5, UF7 and UF8 for Scenario 1. However, MOGWO and MOPSO performed better than LMOGWO for UF2. For Scenario 2, LMOGWO outperformed the other two algorithms for UF5, UF8 and UF9. However, MOGWO performed well for UF2 and UF4. The results of MOPSO were also better than the proposed algorithm for UF4. Moreover, the PM utilization rate (%) was minimized by 30% with LMOGWO, 11% with MOGWO and 10% with MOPSO.

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Cloud–Fog–Based Smart Grid Model for Efficient Resource Management

Cited by 79 publications

References 29 publications

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

Cloudlet Scheduling by Hybridized Monarch Butterfly Optimization Algorithm

An Enhanced Multi-Objective Gray Wolf Optimization for Virtual Machine Placement in Cloud Data Centers

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